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An automatic differentiation package

Project description


autodiff by

Feiyu Chen, Yan Zhao, Yueting Luo

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Automatic differentiation (AD) is a family of techniques for efficiently and accurately evaluating derivatives of numeric functions expressed as computer programs. Application of AD includes Newton’s method for solving nonlinear equations, real-parameter optimization, probabilistic inference, and backpropagation in neural networks. AD has been extremely popular because of the booming development in machine learning and deep learning techniques. Our AD sofeware package enable user to calculate derivatives using the forward and reverse mode.

Installing autodiff

Here is how to install autodiff on command line. We suppose that the user has already installed pip and virtualenv:

  1. clone the project repo by git clone
  2. cd into the local repo and create a virtual environment by virtualenv env
  3. activate the virtual environment by source env/bin/activate (use deactivate to deactivate the virtual environment later.)
  4. install the dependencies by pip install -r requirements.txt
  5. install autodiff by pip install -e .

Getting Started

See milestone2.ipynb under docs/.

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DFYS_autodiff-0.0.1.tar.gz (8.0 kB view hashes)

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